OlympiA: A phase III, multicenter, randomized, placebo-controlled trial of adjuvant olaparib after (neo)adjuvant chemotherapy in patients with germline BRCA1/2 mutations and high-risk HER2-negative early breast cancer.

LBA1 Background: PARP inhibitors (PARPi) target cancers with homologous recombination repair defects by synthetic lethality. The PARPi olaparib (OL) is licensed for metastatic HER2-negative breast cancer with BRCA1/2 germline mutation (gBRCAm). Despite (neo)adjuvant chemotherapy ([N]ACT), recurrence rates in patients (pts) with gBRCAm early breast cancer (EBC) can be high. Novel adjuvant treatments are needed. Methods: OlympiA (NCT02032823), a randomized, double-blind, phase III study, enrolled pts with gBRCAm and HER2-negative (TNBC or hormone-receptor+ [HR+]) high-risk EBC after primary local treatment and ACT/NACT. Eligible pts with TNBC had ≥pT2 or ≥pN1 disease prior to ACT or non-pCR after NACT; those with HR+ BC had ≥4 positive nodes prior to ACT or non-pCR and CPS&EG score ≥3 after NACT. Pts were randomized 1:1 to 1 year of continuous oral OL (300 mg BID) or placebo (PL). Endocrine therapy and bisphosphonates were allowed. The primary endpoint was invasive disease-free survival (IDFS) in the ITT population. Secondary endpoints included distant DFS (DDFS), overall survival (OS) and safety. Safety analysis included adverse events of special interest (AESI) (myelodysplastic syndrome/ acute myeloid leukemia, new primary malignancy, pneumonitis). Per protocol IDMC interim analysis (IA) review was triggered at 165 IDFS events in the first 900 pts, with superiority boundaries based on a hierarchical multiple testing procedure: P < 0.005 for IDFS, followed by P < 0.005 for DDFS and p<0.01 for OS. Results: 1836 pts were enrolled between 06/14–05/19; 49.9% had ACT, 50.1% NACT. Baseline demographics and tumor characteristics were balanced between arms. 82.2% had TNBC; 26.5% received a platinum agent. The IDMC recommended data unblinding as IA showed a significant benefit of OL vs PL for IDFS (hazard ratio [HR] 0.58; 99.5% CI 0.41, 0.82; P < 0.0001) at 2.5 yrs median follow-up. IDFS events occurred in 106/921 and 178/915 pts assigned to OL and PL, respectively. 3-yr IDFS was 85.9% vs 77.1% (diff. 8.8%; 95% CI 4.5%, 13.0%). DDFS was significantly improved with OL (HR 0.57; 99.5% CI 0.39, 0.83; P< 0.0001); 3-yr DDFS was 87.5% vs 80.4% (diff. 7.1%; 95% CI 3.0%, 11.1%). OS was greater for OL than PL but was not statistically significant at IA (HR 0.68; 99.0% CI 0.44, 1.05; P = 0.024); 3-yr OS% 92.0% vs 88.3% (diff. 3.7%; 95% CI 0.3%, 7.1%). Median intended OL exposure was 94.8%. AEs were consistent with the label. G3+ AEs in >1% of OL pts were; anemia (8.7%), neutropenia (4.8%), leukopenia (3.0%), fatigue (1.8%), and lymphocytopenia (1.2%). SAEs and AESI were not increased by OL, SAE 8.7% vs 8.4% and AESI 3.3% vs 5.1%, OL vs PL respectively. Conclusions: Adjuvant OL following ACT or NACT significantly improved IDFS and DDFS with acceptable toxicity in pts with gBRCAm and high-risk HER2-negative EBC. Clinical trial information: NCT02032823.

Overall survival from the phase 3 POLO trial: Maintenance olaparib for germline BRCA-mutated metastatic pancreatic cancer.

378 Background: POLO is the first phase 3 trial to evaluate maintenance therapy with the poly(ADP-ribose) polymerase inhibitor (PARPi) olaparib (O) in patients with metastatic pancreatic cancer (mPaC) and a germline BRCA mutation ( gBRCAm) whose disease had not progressed on first-line platinum-based chemotherapy (PBC). POLO demonstrated that patients had significantly longer progression-free survival (PFS; primary endpoint) with maintenance O than with placebo (P; hazard ratio [HR], 0.53; 95% confidence interval [CI], 0.35–0.82; p= 0.004). Herein, we present final overall survival (OS) data. Methods: POLO was a randomized, double-blind, placebo-controlled trial (NCT02184195) conducted at 119 sites in 12 countries. Eligible patients had mPaC without disease progression for ≥16 weeks on PBC and a deleterious or suspected deleterious gBRCAm. Patients were randomized 3:2 to O (300 mg tablet twice daily) or P. OS (time from randomization until death) was a key secondary endpoint assessed using a log-rank test. A multiple-testing procedure (MTP) was used, with alpha passed to OS owing to a significant PFS result. Time from randomization to second disease progression or death (PFS2), to discontinuation of treatment (TDT), and to initiation of first (TFST) or second (TSST) subsequent therapies following treatment discontinuation or death were secondary endpoints (log-rank test, not in MTP). Primary analysis of OS after 108 deaths; data cut-off (DCO) July 21 2020. Results: Ninety-two and 62 patients were randomized to O and P, respectively; those censored had a median follow-up of 31.3 months (mo) and 23.9 mo, respectively. At DCO, n = 13 remained on O; n = 2 on P. OS was similar for the O and P groups (median 19.0 and 19.2 mo, respectively; HR 0.83 favoring O; 95% CI 0.56–1.22; p= 0.3487). OS at 36 mo was 33.9% for O and 17.8% for P. Median PFS2 was 16.9 mo for O vs 9.3 mo for P (HR, 0.66; 95% CI 0.43–1.02; p= 0.0613). TFST, TSST and TDT were longer with O than P (Table). TDT at 24 mo was 24.3% for O vs 3.3% for P; at 36 mo was 17.2% for O vs 3.3% for P. Incidence of grade ≥3 adverse events (AEs) was 49% for O (anemia most common [12.2%]); 25% for P (anemia, hyperglycemia, upper abdominal pain most common [3.3%]). Treatment was discontinued owing to AEs for 8.9% patients in the O arm vs 1.6% for P. Conclusions: Although HR for OS was in favor of maintenance O vs P among patients with a gBRCAm and mPaC whose disease had not progressed during PBC, there was no statistically significant difference. PFS2 showed a clear trend for treatment benefit beyond disease progression in favor of O, but was not alpha protected. Safety data were consistent with the primary analysis. Clinical trial information: NCT02184195. [Table: see text]

Trends in the extremes of environments associated with severe United States thunderstorms

Severe thunderstorms can have devastating impacts. Concurrently high values of convective available potential energy (CAPE) and storm relative helicity (SRH) are known to be conducive to severe weather, so high values of have been used to indicate high risk of severe thunderstorms. We consider the extreme values of these three variables for a large area of the contiguous United States (US) over the period 1979–2015, and use extreme-value theory and a multiple testing procedure to show that there is a significant time trend in the extremes for PROD maxima in April, May and August, for CAPE maxima in April, May and June, and for maxima of SRH in April and May. These observed increases in CAPE are also relevant for rainfall extremes and are expected in a warmer climate, but have not previously been reported. Moreover, we show that the El Niño-Southern Oscillation explains variation in the extremes of PROD and SRH in February. Our results suggest that the risk from severe thunderstorms in April and May is increasing in parts of the US where it was already high, and that the risk from storms in February is increased over the main part of the region during La Niña years.

Hypotheses on a tree: new error rates and testing strategies

We introduce a multiple testing procedure that controls global error rates at multiple levels of resolution. Conceptually, we frame this problem as the selection of hypotheses that are organized hierarchically in a tree structure. We describe a fast algorithm and prove that it controls relevant error rates given certain assumptions on the dependence between the p-values. Through simulations, we demonstrate that the proposed procedure provides the desired guarantees under a range of dependency structures and that it has the potential to gain power over alternative methods. Finally, we apply the method to studies on the genetic regulation of gene expression across multiple tissues and on the relation between the gut microbiome and colorectal cancer.

Thousands of Alpha Tests

Data snooping is a major concern in empirical asset pricing. We develop a new framework to rigorously perform multiple hypothesis testing in linear asset pricing models, while limiting the occurrence of false positive results typically associated with data snooping. By exploiting a variety of machine learning techniques, our multiple-testing procedure is robust to omitted factors and missing data. We also prove its asymptotic validity when the number of tests is large relative to the sample size, as in many finance applications. To improve the finite sample performance, we also provide a wild-bootstrap procedure for inference and prove its validity in this setting. Finally, we illustrate the empirical relevance in the context of hedge fund performance evaluation.

Testing cell-type-specific mediation effects in genome-wide epigenetic studies

Epigenome-wide mediation analysis aims to identify DNA methylation CpG sites that mediate the causal effects of genetic/environmental exposures on health outcomes. However, DNA methylations in the peripheral blood tissues are usually measured at the bulk level based on a heterogeneous population of white blood cells. Using the bulk level DNA methylation data in mediation analysis might cause confounding bias and reduce study power. Therefore, it is crucial to get fine-grained results by detecting mediation CpG sites in a cell-type-specific way. However, there is a lack of methods and software to achieve this goal. We propose a novel method (Mediation In a Cell-type-Specific fashion, MICS) to identify cell-type-specific mediation effects in genome-wide epigenetic studies using only the bulk-level DNA methylation data. MICS follows the standard mediation analysis paradigm and consists of three key steps. In step1, we assess the exposure-mediator association for each cell type; in step 2, we assess the mediator-outcome association for each cell type; in step 3, we combine the cell-type-specific exposure-mediator and mediator-outcome associations using a multiple testing procedure named MultiMed [Sampson JN, Boca SM, Moore SC, et al. FWER and FDR control when testing multiple mediators. Bioinformatics 2018;34:2418–24] to identify significant CpGs with cell-type-specific mediation effects. We conduct simulation studies to demonstrate that our method has correct FDR control. We also apply the MICS procedure to the Normative Aging Study and identify nine DNA methylation CpG sites in the lymphocytes that might mediate the effect of cigarette smoking on the lung function.

Design, analysis and reporting of multi-arm trials and strategies to address multiple testing

Background It is unclear how multiple treatment comparisons are managed in the analysis of multi-arm trials, particularly related to reducing type I (false positive) and type II errors (false negative). Methods We conducted a cohort study of clinical-trial protocols that were approved by research ethics committees in the UK, Switzerland, Germany and Canada in 2012. We examined the use of multiple-testing procedures to control the overall type I error rate. We created a decision tool to determine the need for multiple-testing procedures. We compared the result of the decision tool to the analysis plan in the protocol. We also compared the pre-specified analysis plans in trial protocols to their publications. Results Sixty-four protocols for multi-arm trials were identified, of which 50 involved multiple testing. Nine of 50 trials (18%) used a single-step multiple-testing procedures such as a Bonferroni correction and 17 (38%) used an ordered sequence of primary comparisons to control the overall type I error. Based on our decision tool, 45 of 50 protocols (90%) required use of a multiple-testing procedure but only 28 of the 45 (62%) accounted for multiplicity in their analysis or provided a rationale if no multiple-testing procedure was used. We identified 32 protocol–publication pairs, of which 8 planned a global-comparison test and 20 planned a multiple-testing procedure in their trial protocol. However, four of these eight trials (50%) did not use the global-comparison test. Likewise, 3 of the 20 trials (15%) did not perform the multiple-testing procedure in the publication. The sample size of our study was small and we did not have access to statistical-analysis plans for the included trials in our study. Conclusions Strategies to reduce type I and type II errors are inconsistently employed in multi-arm trials. Important analytical differences exist between planned analyses in clinical-trial protocols and subsequent publications, which may suggest selective reporting of analyses.